Dynamically consistent reduction of logical regulatory graphs
Theoretical Computer Science
| Hi-index | 0.00 |
Logical modelling constitutes a flexible framework to build qualitative predictive models, which can be readily analysed or simulated as such, and potentially used as scaffolds to build more quantitative (continuous or stochastic) models. We use Multi-valued Decision Diagrams to implement (multi-level) logical updating rules in the modelling software GINsim [2]. This representation enabled the development of efficient algorithms for the identification of stable states, or yet to identify specific (positive or negative) regulatory circuits involved in specific dynamical properties (e.g., multiple attractors or sustained oscillations). To cope with larger molecular networks, we have implemented a flexible reduction method conserving the attractors of the original model into our software GINsim [4]. Furthermore, we have delineated an incremental, compositional strategy to build large models by combining logical models for simpler regulatory modules. These methodological developments have been recently applied to the differentiation of T-helper cells into different subtypes, depending on activation by antigen presenting cells and on micro-environment (combinations of cytokines) [3]. In my lecture, I will introduce the biological background and generic questions regarding the organisation and function of the signalling/regulatory circuits underlying cell fate decision. I will illustrate the types of insights gained by logical modelling through an application dealing with blood cell differentiation and developed in collaboration with Thomas Graf (Center for Genomic Regulation, Barcelona, Spain) [1]. More specifically, I will present a model recapitulating the differentiation of common lymphocyte/myoloid progenitors into T and B lymphocyte committed progenitors in the presence of proper lymphokine signalling. Furthermore, this model enables the reproduction of T or B lymphocyte precursors into macrophages upon induced expression of the transcription factors CEBPa or CEBPb, in the presence of specific lymphokines. Finally, this model can be used to simulate the behaviour of the system in novel situations, e.g. in the presence of combinations of perturbations such as gene knockouts or ectopic expressions.